Ferrite-Based photocatalysts for environmental remediation and solar-driven energy conversion

Ferrite-based materials (spinel MFe₂O₄, orthoferrite RFeO₃, hexaferrite and garnet families) combine visible-light absorption (band gaps approximately 1.2–2.8 eV), intrinsic magnetic recoverability and compositional tunability, making them attractive photocatalysts for environmental and energy applications. Prior reviews have addressed spinel ferrite photocatalysis, but a comprehensive cross-family analysis integrating environmental remediation and solar-driven energy conversion within a unified mechanistic framework has been absent. This review addresses these limitations by systematically surveying over 200 ferrite-based photocatalytic systems across all four structural families, with quantitative benchmarking through compiled apparent rate constants and quantum efficiencies under specified conditions. The review examines crystal structure, electronic band structure and magnetic properties as determinants of photocatalytic behaviour, with particular attention to synthesis-dependent cation distribution. Synthesis methodologies are critically evaluated for their impact on particle size, crystallinity and surface area. An integrated mechanistic framework addresses direct photocatalysis, photo-Fenton chemistry and heterojunction charge transfer (Type-II, Z-scheme, S-scheme) as concurrently operating, composition-dependent pathways. Modification strategies, including cation substitution, morphological engineering and composite formation, are evaluated for their role in enhancing charge separation and spectral response. Applications span organic pollutant degradation, pharmaceutical and emerging contaminant removal, heavy metal reduction, hydrogen evolution and CO₂ photoreduction, including ferrite-specific analysis of pH, catalyst loading, light source and water matrix effects. Systematic reusability analysis incorporating post-cycling physicochemical characterization substantiates the practical advantage of magnetic recoverability. Critical challenges including standardization, scalability, long-term stability and the need for operando mechanistic studies are identified, alongside future directions in machine-learning-guided composition screening and continuous-flow system integration.